There are many known cases of detrimental effects of interference on various active and passive sensing systems. Passive systems are especially vulnerable because they do not transmit any probing signals so that the interference effects cannot be mitigated by simply increasing the transmitted power.
For example, in radio astronomy at the 1.2-1.4 GHz frequency band, spectroscopy and pulsar observations are corrupted by transmissions of ground-based aviation radars. Since radio telescopes operate at the highest possible sensitivity levels, they can detect radar pulses through the antenna side-lobes at distances greater than 100 km. This range may increase significantly when the telescope captures pulses reflected by an object, such as an aircraft.
Another example is microwave radiometry that exploits natural thermal emission produced by the Earth's surface and atmosphere to examine its properties. Unfortunately, the microwave frequency bands utilised for passive remote sensing are also used for communication and surveillance purposes; such applications involve the generation and transmission of high-level microwave signals. The resulting interference manifests itself as the appearance of ‘hot spots’ that will corrupt images of microwave brightness temperature.
A passive energy sensor determines the level of microwave energy by integrating the power of all natural (desired) and man-made (undesired) emissions over a time interval ranging from 10 to 200 milliseconds. Although, in this case, the information of interest is the level of energy originating from natural sources alone, any man-made interference captured by the sensor will also contribute to the result. Therefore, a passive energy sensor is not capable of discriminating between man-made interference and interference originated from natural phenomena.
According to the International Telecommunications Union Recommendation (ITU-R RS.1029-2): “studies have established that measurements in absorption bands are extremely vulnerable to interference because, in general, there is no possibility to detect and to reject data that are contaminated by interference, and because propagation of undetected contaminated data into numerical weather prediction (NWP) models may have a destructive impact on the reliability/quality of weather forecasting”.
If man-made interference is of transient nature, i.e. present during a relatively small fraction of the total observation time, it is possible to excise the intervals containing interference, assuming that such intervals can be detected in a reliable manner. A number of so-called pulse-blanking techniques have been described in the literature; for example, see: N. Niamsuvan et al., “Examination of a simple pulse-blanking technique for radar interference mitigation,” Radio Sci., vol. 40, RS5S03, 2005.
One useful pulse-blanking scheme is presented in: Q. Zhang et al., “Combating Pulsed Radar Interference in Radio Astronomy,” Astronom J., pp. 1588-1594, September 2003. The proposed interference rejection method is based on replacing corrupted samples by zeros, and retaining all other original samples. As a result, a fast Fourier transform (FFT) is performed on a data frame of the original ‘length’ but also including a number of all-zero blocks.
Irrespective of a particular scheme of pulse blanking, its efficacy will depend on reliable detection of man-made interference. It should be pointed out that the signal level itself cannot be exploited to detect interference because high-level transients may also originate from natural phenomena. Therefore, an optimal pulse-blanking scheme should incorporate some form of a level detector followed by a classifier capable of discriminating between man-made interference and signals of natural origin.
In general, many man-made transmissions have a form of a pulsed sine-wave carrier modulated in phase or frequency. Discrete-time samples of such a signal obtained from asynchronous sampling will have the same statistical properties as those of a sampled sine wave with a random phase. On the other hand, in general, signals generated by natural phenomena may be represented by a random Gaussian process.
An interference classifier is therefore required to determine whether an observed signal represents a Gaussian random process or a noisy sine wave with randomly varying phase.
Such an interference classifier also has applications in cognitive radio networks. A requirement for improving such networks is a high quality spectrum sensing device to detect an unused spectrum in order to share it without any harmful interference with other users. Because simple energy detectors cannot provide the reliable detection of signal presence, more sophisticated methods are required.
In the above and other applications, a signal being processed comprises a dominant-frequency waveform combined with noise and also man-made interference of transient nature. The presence of transient interference will significantly increase the level of background noise in the frequency domain. Consequently, in conventional systems, a reliable detection of small frequency components will be practically impossible. Therefore, any frequency analysis method to be of practical use should incorporate some means of efficient rejection or suppression of interference.
For example in automotive frequency-modulated continuous-wave (FMCW) radar, effects of transient interference of any type may be reduced by exploiting signal blanking. However, while noise bursts can only be suppressed by employing signal blanking, chirp interference effects may additionally be mitigated by changing in a suitable manner the characteristics of a transmitted waveform. Therefore, it is of practical importance to discriminate between these two classes of transient interference.
A known type of interference classifier is based on determining a value of kurtosis from a set of samples under examination. Kurtosis is defined as the ratio of the fourth central moment to the square of the second central moment. Accordingly, in a case of K zero-mean signal samples x1, x2, . . . , xk, . . . , xK, an empirical kurtosis KX can be determined from:
                              K          X                =                              (                                          1                K                            ⁢                                                ∑                                      k                    =                    1                                    K                                ⁢                                  x                  k                  4                                                      )                    [                                    (                                                1                  K                                ⁢                                                      ∑                                          k                      =                      1                                        K                                    ⁢                                      x                    k                    2                                                              )                                      -              2                                ]                                    Eqn        .                                  ⁢        1            
It is known that for noise, modelled by a random Gaussian process, the kurtosis is equal to three, independent of the noise level. However, in the case of a randomly sampled sine wave of any amplitude, the kurtosis is equal to 1.5. Therefore, the value of empirical kurtosis, determined from a set of samples under examination, can be compared to a predetermined threshold to decide whether the set is more likely to represent noise or rather a randomly sampled sine wave.
The performance of a kurtosis-based interference classifier has been analyzed in a number of publications; for example, see: R. D. De Roo et al., “Sensitivity of the Kurtosis Statistic as a Detector of Pulsed Sinusoidal RFI,” IEEE Trans. Geosci. Remote Sens., pp. 1938-1946, July 2007.
A recent review of various state-of-the-art methods is presented in: S. Misra et al., “Microwave Radiometer Radio-Frequency Interference Detection Algorithms: A Comparative Study,” IEEE Trans. Geosci. Remote Sens., pp. 3742-3754, November 2009.
Although various ad-hoc interference detection and classification procedures have been exploited in many known systems, no statistically optimal classifiers have been devised.
An improved method and apparatus for discriminating between man-made interference and noise interference of natural origin is therefore desired.